{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# vitalAperiodic\n",
    "\n",
    "The vitalAperiodic table provides invasive vital sign data which is interfaced into eCareManager at irregular intervals. Unlike most tables in eICU-CRD, vitalAperiodic does not use an entity-attribute-value model, but rather has an individual column to capture each data element. Columns available include:\n",
    "\n",
    "* Blood pressures: nonInvasiveSystolic, nonInvasiveDiastolic, nonInvasiveMean\n",
    "* Cardiac output measures: cardiacOutput, cardiacInput\n",
    "* Systemic circulation measures: svr, svri, pvr, pvri\n",
    "* Pulmonary pressures: pulmonary artery occlusion pressure (paop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "import pdvega\n",
    "\n",
    "# for configuring connection \n",
    "from configobj import ConfigObj\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database: eicu\n",
      "Username: alistairewj\n"
     ]
    }
   ],
   "source": [
    "# Create a database connection using settings from config file\n",
    "config='../db/config.ini'\n",
    "\n",
    "# connection info\n",
    "conn_info = dict()\n",
    "if os.path.isfile(config):\n",
    "    config = ConfigObj(config)\n",
    "    conn_info[\"sqluser\"] = config['username']\n",
    "    conn_info[\"sqlpass\"] = config['password']\n",
    "    conn_info[\"sqlhost\"] = config['host']\n",
    "    conn_info[\"sqlport\"] = config['port']\n",
    "    conn_info[\"dbname\"] = config['dbname']\n",
    "    conn_info[\"schema_name\"] = config['schema_name']\n",
    "else:\n",
    "    conn_info[\"sqluser\"] = 'postgres'\n",
    "    conn_info[\"sqlpass\"] = ''\n",
    "    conn_info[\"sqlhost\"] = 'localhost'\n",
    "    conn_info[\"sqlport\"] = 5432\n",
    "    conn_info[\"dbname\"] = 'eicu'\n",
    "    conn_info[\"schema_name\"] = 'public,eicu_crd'\n",
    "    \n",
    "# Connect to the eICU database\n",
    "print('Database: {}'.format(conn_info['dbname']))\n",
    "print('Username: {}'.format(conn_info[\"sqluser\"]))\n",
    "if conn_info[\"sqlpass\"] == '':\n",
    "    # try connecting without password, i.e. peer or OS authentication\n",
    "    try:\n",
    "        if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   user=conn_info[\"sqluser\"])            \n",
    "        else:\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   host=conn_info[\"sqlhost\"],\n",
    "                                   port=conn_info[\"sqlport\"],\n",
    "                                   user=conn_info[\"sqluser\"])\n",
    "    except:\n",
    "        conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n",
    "\n",
    "        con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                               host=conn_info[\"sqlhost\"],\n",
    "                               port=conn_info[\"sqlport\"],\n",
    "                               user=conn_info[\"sqluser\"],\n",
    "                               password=conn_info[\"sqlpass\"])\n",
    "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine a single patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "patientunitstayid = 145467"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientunitstayid</th>\n",
       "      <th>vitalaperiodicid</th>\n",
       "      <th>observationyear</th>\n",
       "      <th>observationtime24</th>\n",
       "      <th>observationtime</th>\n",
       "      <th>noninvasivesystolic</th>\n",
       "      <th>noninvasivediastolic</th>\n",
       "      <th>noninvasivemean</th>\n",
       "      <th>paop</th>\n",
       "      <th>cardiacoutput</th>\n",
       "      <th>cardiacinput</th>\n",
       "      <th>svr</th>\n",
       "      <th>svri</th>\n",
       "      <th>pvr</th>\n",
       "      <th>pvri</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>observationoffset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>145467</td>\n",
       "      <td>4223721</td>\n",
       "      <td>2014</td>\n",
       "      <td>19:33:34</td>\n",
       "      <td>night</td>\n",
       "      <td>113.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>145467</td>\n",
       "      <td>4223722</td>\n",
       "      <td>2014</td>\n",
       "      <td>20:13:06</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1496.0</td>\n",
       "      <td>3099.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>255.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>145467</td>\n",
       "      <td>4223723</td>\n",
       "      <td>2014</td>\n",
       "      <td>20:59:54</td>\n",
       "      <td>night</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>4.3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>2810.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>145467</td>\n",
       "      <td>4223724</td>\n",
       "      <td>2014</td>\n",
       "      <td>00:04:04</td>\n",
       "      <td>midnight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>2097.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>145467</td>\n",
       "      <td>4223725</td>\n",
       "      <td>2014</td>\n",
       "      <td>06:40:46</td>\n",
       "      <td>midday</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>7.8</td>\n",
       "      <td>3.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   patientunitstayid  vitalaperiodicid  observationyear  \\\n",
       "observationoffset                                                         \n",
       "4                             145467           4223721             2014   \n",
       "44                            145467           4223722             2014   \n",
       "90                            145467           4223723             2014   \n",
       "275                           145467           4223724             2014   \n",
       "671                           145467           4223725             2014   \n",
       "\n",
       "                  observationtime24 observationtime  noninvasivesystolic  \\\n",
       "observationoffset                                                          \n",
       "4                          19:33:34           night                113.0   \n",
       "44                         20:13:06           night                  NaN   \n",
       "90                         20:59:54           night                  NaN   \n",
       "275                        00:04:04        midnight                  NaN   \n",
       "671                        06:40:46          midday                  NaN   \n",
       "\n",
       "                   noninvasivediastolic  noninvasivemean  paop  cardiacoutput  \\\n",
       "observationoffset                                                               \n",
       "4                                  62.0             81.0  None            NaN   \n",
       "44                                  NaN              NaN  None            3.9   \n",
       "90                                  NaN              NaN  None            4.3   \n",
       "275                                 NaN              NaN  None            4.5   \n",
       "671                                 NaN              NaN  None            7.8   \n",
       "\n",
       "                   cardiacinput     svr    svri    pvr   pvri  \n",
       "observationoffset                                              \n",
       "4                           NaN     NaN     NaN    NaN    NaN  \n",
       "44                          1.9  1496.0  3099.0  123.0  255.0  \n",
       "90                          2.1  1357.0  2810.0  112.0  231.0  \n",
       "275                         2.2  1012.0  2097.0  107.0  221.0  \n",
       "671                         3.8     NaN     NaN   72.0  149.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select *\n",
    "from vitalaperiodic\n",
    "where patientunitstayid = {}\n",
    "order by observationoffset\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df.set_index('observationoffset', inplace=True)\n",
    "df.sort_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"1863871e-de60-450d-94f6-bc4dda43a473\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#1863871e-de60-450d-94f6-bc4dda43a473"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"height\": 300, \"mark\": \"line\", \"data\": {\"values\": [{\"observationoffset\": 4, \"variable\": \"noninvasivesystolic\", \"value\": 113.0}, {\"observationoffset\": 44, \"variable\": \"noninvasivesystolic\", \"value\": null}, {\"observationoffset\": 90, \"variable\": \"noninvasivesystolic\", \"value\": null}, {\"observationoffset\": 275, \"variable\": \"noninvasivesystolic\", \"value\": null}, {\"observationoffset\": 671, \"variable\": \"noninvasivesystolic\", \"value\": null}, {\"observationoffset\": 756, \"variable\": \"noninvasivesystolic\", \"value\": null}, {\"observationoffset\": 781, \"variable\": \"noninvasivesystolic\", \"value\": 133.0}, {\"observationoffset\": 1850, \"variable\": \"noninvasivesystolic\", \"value\": 127.0}, {\"observationoffset\": 1860, \"variable\": \"noninvasivesystolic\", \"value\": 139.0}, {\"observationoffset\": 2432, \"variable\": \"noninvasivesystolic\", \"value\": 124.0}, {\"observationoffset\": 2604, \"variable\": \"noninvasivesystolic\", \"value\": 147.0}, {\"observationoffset\": 2635, \"variable\": \"noninvasivesystolic\", \"value\": 132.0}, {\"observationoffset\": 2664, \"variable\": \"noninvasivesystolic\", \"value\": 130.0}, {\"observationoffset\": 2671, \"variable\": \"noninvasivesystolic\", \"value\": 132.0}, {\"observationoffset\": 2731, \"variable\": \"noninvasivesystolic\", \"value\": 139.0}, {\"observationoffset\": 2793, \"variable\": \"noninvasivesystolic\", \"value\": 171.0}, {\"observationoffset\": 2804, \"variable\": \"noninvasivesystolic\", \"value\": 144.0}, {\"observationoffset\": 2851, \"variable\": \"noninvasivesystolic\", \"value\": 141.0}, {\"observationoffset\": 2863, \"variable\": \"noninvasivesystolic\", \"value\": 148.0}, {\"observationoffset\": 2877, \"variable\": \"noninvasivesystolic\", \"value\": 138.0}, {\"observationoffset\": 2911, \"variable\": \"noninvasivesystolic\", \"value\": 135.0}, {\"observationoffset\": 2971, \"variable\": \"noninvasivesystolic\", \"value\": 142.0}, {\"observationoffset\": 3031, \"variable\": \"noninvasivesystolic\", \"value\": 160.0}, {\"observationoffset\": 3099, \"variable\": \"noninvasivesystolic\", \"value\": 154.0}, {\"observationoffset\": 3172, \"variable\": \"noninvasivesystolic\", \"value\": 145.0}, {\"observationoffset\": 3211, \"variable\": \"noninvasivesystolic\", \"value\": 147.0}, {\"observationoffset\": 3271, \"variable\": \"noninvasivesystolic\", \"value\": 159.0}, {\"observationoffset\": 3331, \"variable\": \"noninvasivesystolic\", \"value\": 157.0}, {\"observationoffset\": 3391, \"variable\": \"noninvasivesystolic\", \"value\": 158.0}, {\"observationoffset\": 3451, \"variable\": \"noninvasivesystolic\", \"value\": 155.0}, {\"observationoffset\": 3511, \"variable\": \"noninvasivesystolic\", \"value\": 156.0}, {\"observationoffset\": 3571, \"variable\": \"noninvasivesystolic\", \"value\": 150.0}, {\"observationoffset\": 3631, \"variable\": \"noninvasivesystolic\", \"value\": 148.0}, {\"observationoffset\": 3691, \"variable\": \"noninvasivesystolic\", \"value\": 155.0}, {\"observationoffset\": 3751, \"variable\": \"noninvasivesystolic\", \"value\": 158.0}, {\"observationoffset\": 3811, \"variable\": \"noninvasivesystolic\", \"value\": 160.0}, {\"observationoffset\": 3871, \"variable\": \"noninvasivesystolic\", \"value\": 169.0}, {\"observationoffset\": 3922, \"variable\": \"noninvasivesystolic\", \"value\": 144.0}, {\"observationoffset\": 3931, \"variable\": \"noninvasivesystolic\", \"value\": 153.0}, {\"observationoffset\": 3991, \"variable\": \"noninvasivesystolic\", \"value\": 162.0}, {\"observationoffset\": 4051, \"variable\": \"noninvasivesystolic\", \"value\": 163.0}, {\"observationoffset\": 4059, \"variable\": \"noninvasivesystolic\", \"value\": 158.0}, {\"observationoffset\": 4071, \"variable\": \"noninvasivesystolic\", \"value\": 167.0}, {\"observationoffset\": 4120, \"variable\": \"noninvasivesystolic\", \"value\": 178.0}, {\"observationoffset\": 4135, \"variable\": \"noninvasivesystolic\", \"value\": 142.0}, {\"observationoffset\": 4223, \"variable\": \"noninvasivesystolic\", \"value\": 148.0}, {\"observationoffset\": 4303, \"variable\": \"noninvasivesystolic\", \"value\": 137.0}, {\"observationoffset\": 4317, \"variable\": \"noninvasivesystolic\", \"value\": 141.0}, {\"observationoffset\": 4508, \"variable\": \"noninvasivesystolic\", \"value\": 147.0}, {\"observationoffset\": 4606, \"variable\": \"noninvasivesystolic\", \"value\": 156.0}, {\"observationoffset\": 4627, \"variable\": \"noninvasivesystolic\", \"value\": 133.0}, {\"observationoffset\": 4684, \"variable\": \"noninvasivesystolic\", \"value\": 143.0}, {\"observationoffset\": 4, \"variable\": \"noninvasivediastolic\", \"value\": 62.0}, {\"observationoffset\": 44, \"variable\": \"noninvasivediastolic\", \"value\": null}, {\"observationoffset\": 90, \"variable\": \"noninvasivediastolic\", \"value\": null}, {\"observationoffset\": 275, \"variable\": \"noninvasivediastolic\", \"value\": null}, {\"observationoffset\": 671, \"variable\": \"noninvasivediastolic\", \"value\": null}, {\"observationoffset\": 756, \"variable\": \"noninvasivediastolic\", \"value\": null}, {\"observationoffset\": 781, \"variable\": \"noninvasivediastolic\", \"value\": 63.0}, {\"observationoffset\": 1850, \"variable\": \"noninvasivediastolic\", \"value\": 61.0}, {\"observationoffset\": 1860, \"variable\": \"noninvasivediastolic\", \"value\": 65.0}, {\"observationoffset\": 2432, \"variable\": \"noninvasivediastolic\", \"value\": 58.0}, {\"observationoffset\": 2604, \"variable\": \"noninvasivediastolic\", \"value\": 68.0}, {\"observationoffset\": 2635, \"variable\": \"noninvasivediastolic\", \"value\": 60.0}, {\"observationoffset\": 2664, \"variable\": \"noninvasivediastolic\", \"value\": 64.0}, {\"observationoffset\": 2671, \"variable\": \"noninvasivediastolic\", \"value\": 62.0}, {\"observationoffset\": 2731, \"variable\": \"noninvasivediastolic\", \"value\": 72.0}, {\"observationoffset\": 2793, \"variable\": \"noninvasivediastolic\", \"value\": 77.0}, {\"observationoffset\": 2804, \"variable\": \"noninvasivediastolic\", \"value\": 73.0}, {\"observationoffset\": 2851, \"variable\": \"noninvasivediastolic\", \"value\": 67.0}, {\"observationoffset\": 2863, \"variable\": \"noninvasivediastolic\", \"value\": 71.0}, {\"observationoffset\": 2877, \"variable\": \"noninvasivediastolic\", \"value\": 68.0}, {\"observationoffset\": 2911, \"variable\": \"noninvasivediastolic\", \"value\": 67.0}, {\"observationoffset\": 2971, \"variable\": \"noninvasivediastolic\", \"value\": 74.0}, {\"observationoffset\": 3031, \"variable\": \"noninvasivediastolic\", \"value\": 82.0}, {\"observationoffset\": 3099, \"variable\": \"noninvasivediastolic\", \"value\": 73.0}, {\"observationoffset\": 3172, \"variable\": \"noninvasivediastolic\", \"value\": 74.0}, {\"observationoffset\": 3211, \"variable\": \"noninvasivediastolic\", \"value\": 71.0}, {\"observationoffset\": 3271, \"variable\": \"noninvasivediastolic\", \"value\": 80.0}, {\"observationoffset\": 3331, \"variable\": \"noninvasivediastolic\", \"value\": 75.0}, {\"observationoffset\": 3391, \"variable\": \"noninvasivediastolic\", \"value\": 77.0}, {\"observationoffset\": 3451, \"variable\": \"noninvasivediastolic\", \"value\": 76.0}, {\"observationoffset\": 3511, \"variable\": \"noninvasivediastolic\", \"value\": 77.0}, {\"observationoffset\": 3571, \"variable\": \"noninvasivediastolic\", \"value\": 71.0}, {\"observationoffset\": 3631, \"variable\": \"noninvasivediastolic\", \"value\": 74.0}, {\"observationoffset\": 3691, \"variable\": \"noninvasivediastolic\", \"value\": 73.0}, {\"observationoffset\": 3751, \"variable\": \"noninvasivediastolic\", \"value\": 75.0}, {\"observationoffset\": 3811, \"variable\": \"noninvasivediastolic\", \"value\": 76.0}, {\"observationoffset\": 3871, \"variable\": \"noninvasivediastolic\", \"value\": 81.0}, {\"observationoffset\": 3922, \"variable\": \"noninvasivediastolic\", \"value\": 76.0}, {\"observationoffset\": 3931, \"variable\": \"noninvasivediastolic\", \"value\": 81.0}, {\"observationoffset\": 3991, \"variable\": \"noninvasivediastolic\", \"value\": 86.0}, {\"observationoffset\": 4051, \"variable\": \"noninvasivediastolic\", \"value\": 81.0}, {\"observationoffset\": 4059, \"variable\": \"noninvasivediastolic\", \"value\": 79.0}, {\"observationoffset\": 4071, \"variable\": \"noninvasivediastolic\", \"value\": 85.0}, {\"observationoffset\": 4120, \"variable\": \"noninvasivediastolic\", \"value\": 79.0}, {\"observationoffset\": 4135, \"variable\": \"noninvasivediastolic\", \"value\": 67.0}, {\"observationoffset\": 4223, \"variable\": \"noninvasivediastolic\", \"value\": 74.0}, {\"observationoffset\": 4303, \"variable\": \"noninvasivediastolic\", \"value\": 78.0}, {\"observationoffset\": 4317, \"variable\": \"noninvasivediastolic\", \"value\": 75.0}, {\"observationoffset\": 4508, \"variable\": \"noninvasivediastolic\", \"value\": 84.0}, {\"observationoffset\": 4606, \"variable\": \"noninvasivediastolic\", \"value\": 72.0}, {\"observationoffset\": 4627, \"variable\": \"noninvasivediastolic\", \"value\": 72.0}, {\"observationoffset\": 4684, \"variable\": \"noninvasivediastolic\", \"value\": 77.0}, {\"observationoffset\": 4, \"variable\": \"noninvasivemean\", \"value\": 81.0}, {\"observationoffset\": 44, \"variable\": \"noninvasivemean\", \"value\": null}, {\"observationoffset\": 90, \"variable\": \"noninvasivemean\", \"value\": null}, {\"observationoffset\": 275, \"variable\": \"noninvasivemean\", \"value\": null}, {\"observationoffset\": 671, \"variable\": \"noninvasivemean\", \"value\": null}, {\"observationoffset\": 756, \"variable\": \"noninvasivemean\", \"value\": null}, {\"observationoffset\": 781, \"variable\": \"noninvasivemean\", \"value\": 91.0}, {\"observationoffset\": 1850, \"variable\": \"noninvasivemean\", \"value\": 87.0}, {\"observationoffset\": 1860, \"variable\": \"noninvasivemean\", \"value\": 94.0}, {\"observationoffset\": 2432, \"variable\": \"noninvasivemean\", \"value\": 83.0}, {\"observationoffset\": 2604, \"variable\": \"noninvasivemean\", \"value\": 98.0}, {\"observationoffset\": 2635, \"variable\": \"noninvasivemean\", \"value\": 87.0}, {\"observationoffset\": 2664, \"variable\": \"noninvasivemean\", \"value\": 89.0}, {\"observationoffset\": 2671, \"variable\": \"noninvasivemean\", \"value\": 88.0}, {\"observationoffset\": 2731, \"variable\": \"noninvasivemean\", \"value\": 98.0}, {\"observationoffset\": 2793, \"variable\": \"noninvasivemean\", \"value\": 110.0}, {\"observationoffset\": 2804, \"variable\": \"noninvasivemean\", \"value\": 100.0}, {\"observationoffset\": 2851, \"variable\": \"noninvasivemean\", \"value\": 96.0}, {\"observationoffset\": 2863, \"variable\": \"noninvasivemean\", \"value\": 102.0}, {\"observationoffset\": 2877, \"variable\": \"noninvasivemean\", \"value\": 94.0}, {\"observationoffset\": 2911, \"variable\": \"noninvasivemean\", \"value\": 92.0}, {\"observationoffset\": 2971, \"variable\": \"noninvasivemean\", \"value\": 101.0}, {\"observationoffset\": 3031, \"variable\": \"noninvasivemean\", \"value\": 113.0}, {\"observationoffset\": 3099, \"variable\": \"noninvasivemean\", \"value\": 105.0}, {\"observationoffset\": 3172, \"variable\": \"noninvasivemean\", \"value\": 103.0}, {\"observationoffset\": 3211, \"variable\": \"noninvasivemean\", \"value\": 99.0}, {\"observationoffset\": 3271, \"variable\": \"noninvasivemean\", \"value\": 112.0}, {\"observationoffset\": 3331, \"variable\": \"noninvasivemean\", \"value\": 107.0}, {\"observationoffset\": 3391, \"variable\": \"noninvasivemean\", \"value\": 109.0}, {\"observationoffset\": 3451, \"variable\": \"noninvasivemean\", \"value\": 107.0}, {\"observationoffset\": 3511, \"variable\": \"noninvasivemean\", \"value\": 107.0}, {\"observationoffset\": 3571, \"variable\": \"noninvasivemean\", \"value\": 100.0}, {\"observationoffset\": 3631, \"variable\": \"noninvasivemean\", \"value\": 104.0}, {\"observationoffset\": 3691, \"variable\": \"noninvasivemean\", \"value\": 105.0}, {\"observationoffset\": 3751, \"variable\": \"noninvasivemean\", \"value\": 108.0}, {\"observationoffset\": 3811, \"variable\": \"noninvasivemean\", \"value\": 109.0}, {\"observationoffset\": 3871, \"variable\": \"noninvasivemean\", \"value\": 113.0}, {\"observationoffset\": 3922, \"variable\": \"noninvasivemean\", \"value\": 101.0}, {\"observationoffset\": 3931, \"variable\": \"noninvasivemean\", \"value\": 110.0}, {\"observationoffset\": 3991, \"variable\": \"noninvasivemean\", \"value\": 117.0}, {\"observationoffset\": 4051, \"variable\": \"noninvasivemean\", \"value\": 114.0}, {\"observationoffset\": 4059, \"variable\": \"noninvasivemean\", \"value\": 110.0}, {\"observationoffset\": 4071, \"variable\": \"noninvasivemean\", \"value\": 113.0}, {\"observationoffset\": 4120, \"variable\": \"noninvasivemean\", \"value\": 114.0}, {\"observationoffset\": 4135, \"variable\": \"noninvasivemean\", \"value\": 97.0}, {\"observationoffset\": 4223, \"variable\": \"noninvasivemean\", \"value\": 102.0}, {\"observationoffset\": 4303, \"variable\": \"noninvasivemean\", \"value\": 101.0}, {\"observationoffset\": 4317, \"variable\": \"noninvasivemean\", \"value\": 102.0}, {\"observationoffset\": 4508, \"variable\": \"noninvasivemean\", \"value\": 111.0}, {\"observationoffset\": 4606, \"variable\": \"noninvasivemean\", \"value\": 104.0}, {\"observationoffset\": 4627, \"variable\": \"noninvasivemean\", \"value\": 96.0}, {\"observationoffset\": 4684, \"variable\": \"noninvasivemean\", \"value\": 105.0}, {\"observationoffset\": 4, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 44, \"variable\": \"cardiacoutput\", \"value\": 3.9000001}, {\"observationoffset\": 90, \"variable\": \"cardiacoutput\", \"value\": 4.3000002}, {\"observationoffset\": 275, \"variable\": \"cardiacoutput\", \"value\": 4.5}, {\"observationoffset\": 671, \"variable\": \"cardiacoutput\", \"value\": 7.8000002}, {\"observationoffset\": 756, \"variable\": \"cardiacoutput\", \"value\": 7.4000001}, {\"observationoffset\": 781, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 1850, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 1860, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2432, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2604, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2635, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2664, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2671, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2731, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2793, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2804, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2851, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2863, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2877, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2911, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 2971, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3031, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3099, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3172, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3211, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3271, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3331, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3391, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3451, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3511, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3571, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3631, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3691, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3751, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3811, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3871, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3922, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3931, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 3991, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4051, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4059, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4071, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4120, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4135, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4223, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4303, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4317, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4508, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4606, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4627, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4684, \"variable\": \"cardiacoutput\", \"value\": null}, {\"observationoffset\": 4, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 44, \"variable\": \"cardiacinput\", \"value\": 1.9}, {\"observationoffset\": 90, \"variable\": \"cardiacinput\", \"value\": 2.0999999}, {\"observationoffset\": 275, \"variable\": \"cardiacinput\", \"value\": 2.2}, {\"observationoffset\": 671, \"variable\": \"cardiacinput\", \"value\": 3.8}, {\"observationoffset\": 756, \"variable\": \"cardiacinput\", \"value\": 3.5999999}, {\"observationoffset\": 781, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 1850, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 1860, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2432, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2604, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2635, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2664, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2671, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2731, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2793, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2804, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2851, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2863, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2877, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2911, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 2971, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3031, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3099, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3172, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3211, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3271, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3331, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3391, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3451, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3511, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3571, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3631, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3691, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3751, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3811, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3871, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3922, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3931, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 3991, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4051, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4059, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4071, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4120, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4135, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4223, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4303, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4317, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4508, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4606, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4627, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4684, \"variable\": \"cardiacinput\", \"value\": null}, {\"observationoffset\": 4, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 44, \"variable\": \"pvr\", \"value\": 123.0}, {\"observationoffset\": 90, \"variable\": \"pvr\", \"value\": 112.0}, {\"observationoffset\": 275, \"variable\": \"pvr\", \"value\": 107.0}, {\"observationoffset\": 671, \"variable\": \"pvr\", \"value\": 72.0}, {\"observationoffset\": 756, \"variable\": \"pvr\", \"value\": 86.0}, {\"observationoffset\": 781, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 1850, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 1860, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2432, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2604, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2635, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2664, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2671, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2731, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2793, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2804, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2851, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2863, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2877, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2911, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 2971, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3031, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3099, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3172, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3211, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3271, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3331, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3391, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3451, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3511, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3571, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3631, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3691, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3751, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3811, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3871, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3922, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3931, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 3991, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4051, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4059, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4071, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4120, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4135, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4223, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4303, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4317, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4508, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4606, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4627, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4684, \"variable\": \"pvr\", \"value\": null}, {\"observationoffset\": 4, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 44, \"variable\": \"pvri\", \"value\": 255.0}, {\"observationoffset\": 90, \"variable\": \"pvri\", \"value\": 231.0}, {\"observationoffset\": 275, \"variable\": \"pvri\", \"value\": 221.0}, {\"observationoffset\": 671, \"variable\": \"pvri\", \"value\": 149.0}, {\"observationoffset\": 756, \"variable\": \"pvri\", \"value\": 179.0}, {\"observationoffset\": 781, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 1850, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 1860, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2432, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2604, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2635, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2664, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2671, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2731, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2793, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2804, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2851, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2863, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2877, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2911, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 2971, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3031, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3099, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3172, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3211, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3271, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3331, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3391, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3451, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3511, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3571, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3631, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3691, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3751, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3811, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3871, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3922, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3931, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 3991, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4051, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4059, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4071, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4120, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4135, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4223, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4303, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4317, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4508, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4606, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4627, \"variable\": \"pvri\", \"value\": null}, {\"observationoffset\": 4684, \"variable\": \"pvri\", \"value\": null}]}, \"selection\": {\"grid\": {\"bind\": \"scales\", \"type\": \"interval\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.json\", \"width\": 450, \"encoding\": {\"x\": {\"field\": \"observationoffset\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"value\", \"type\": \"quantitative\"}, \"color\": {\"field\": \"variable\", \"type\": \"nominal\"}}};\n",
       "var selector = \"#1863871e-de60-450d-94f6-bc4dda43a473\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#1863871e-de60-450d-94f6-bc4dda43a473"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega3": "#1863871e-de60-450d-94f6-bc4dda43a473"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list of columns to plot\n",
    "vitals = ['noninvasivesystolic', 'noninvasivediastolic', 'noninvasivemean',\n",
    "          #'paop',\n",
    "          'cardiacoutput', 'cardiacinput',\n",
    "          'pvr', 'pvri']\n",
    "# we exclude  'svr', 'svri' from the plot as their scale is too high\n",
    "# we exclude 'paop' as it's all none\n",
    "df[vitals].vgplot.line()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hospitals with data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>number_of_patients_with_tbl</th>\n",
       "      <th>data completion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>73</td>\n",
       "      <td>7059</td>\n",
       "      <td>6635</td>\n",
       "      <td>93.993483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>167</td>\n",
       "      <td>6092</td>\n",
       "      <td>5585</td>\n",
       "      <td>91.677610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>264</td>\n",
       "      <td>5237</td>\n",
       "      <td>5018</td>\n",
       "      <td>95.818217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>420</td>\n",
       "      <td>4679</td>\n",
       "      <td>4432</td>\n",
       "      <td>94.721094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>338</td>\n",
       "      <td>4277</td>\n",
       "      <td>4181</td>\n",
       "      <td>97.755436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>243</td>\n",
       "      <td>4243</td>\n",
       "      <td>4105</td>\n",
       "      <td>96.747584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>176</td>\n",
       "      <td>4328</td>\n",
       "      <td>3931</td>\n",
       "      <td>90.827172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>199</td>\n",
       "      <td>4240</td>\n",
       "      <td>3890</td>\n",
       "      <td>91.745283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>458</td>\n",
       "      <td>3701</td>\n",
       "      <td>3642</td>\n",
       "      <td>98.405836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>208</td>\n",
       "      <td>3650</td>\n",
       "      <td>3581</td>\n",
       "      <td>98.109589</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     hospitalid  number_of_patients  number_of_patients_with_tbl  \\\n",
       "11           73                7059                         6635   \n",
       "54          167                6092                         5585   \n",
       "106         264                5237                         5018   \n",
       "184         420                4679                         4432   \n",
       "134         338                4277                         4181   \n",
       "90          243                4243                         4105   \n",
       "58          176                4328                         3931   \n",
       "71          199                4240                         3890   \n",
       "206         458                3701                         3642   \n",
       "80          208                3650                         3581   \n",
       "\n",
       "     data completion  \n",
       "11         93.993483  \n",
       "54         91.677610  \n",
       "106        95.818217  \n",
       "184        94.721094  \n",
       "134        97.755436  \n",
       "90         96.747584  \n",
       "58         90.827172  \n",
       "71         91.745283  \n",
       "206        98.405836  \n",
       "80         98.109589  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "with t as\n",
    "(\n",
    "select distinct patientunitstayid\n",
    "from vitalaperiodic\n",
    ")\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct t.patientunitstayid) as number_of_patients_with_tbl\n",
    "from patient pt\n",
    "left join t\n",
    "  on pt.patientunitstayid = t.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format(patientunitstayid)\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "df['data completion'] = df['number_of_patients_with_tbl'] / df['number_of_patients'] * 100.0\n",
    "df.sort_values('number_of_patients_with_tbl', ascending=False, inplace=True)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"14718fa3-9ada-4901-9cad-f3ce250d0a18\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#14718fa3-9ada-4901-9cad-f3ce250d0a18"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"height\": 300, \"mark\": \"bar\", \"data\": {\"values\": [{\"Percent of patients with data\": 93.99348349624591, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.67760998030204, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 95.81821653618483, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.72109425090831, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.7554360533084, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.74758425642234, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 90.82717190388169, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.74528301886792, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.40583626047015, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.10958904109589, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.64998617638926, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 95.30685920577618, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.62681696825868, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.80994060643951, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.31383883032983, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.69570913855225, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 74.04266958424508, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.8149947934745, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 90.63056644104026, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.49861714737258, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.2210700953961, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.5781990521327, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.90033381020505, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.43577545195052, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.5215723873442, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.20841300191205, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.58699019101704, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.96342637151108, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.17434373452204, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 93.92857142857143, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.32703723691311, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.96802646085997, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.58698092031426, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.91922639362912, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.29244249726177, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.16567342073897, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 86.88, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.98626216370923, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.4937343358396, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.9795918367347, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 72.22482435597189, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.378728923476, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.93390614672836, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 93.67245657568239, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.44903581267218, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.57313037723361, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.40209267563527, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.84767707539984, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.26086956521739, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.48625294579732, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.89559164733178, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.10144927536231, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.98989898989899, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.55195911413969, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.97253634894992, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.11894273127754, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.09747292418773, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.71794871794873, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.00947867298578, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 90.20979020979021, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.02248289345064, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.79838709677419, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.6951219512195, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.4714151827554, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.79402677651905, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.9633401221996, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.36043095004896, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.26082365364309, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 93.31337325349301, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.30603448275862, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.78453038674033, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.19908466819221, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.83132530120481, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.29476248477467, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.66666666666666, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.79194630872483, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.0578734858681, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.71052631578947, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 84.66042154566745, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.03713892709766, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.89807162534436, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.72027972027972, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.15730337078652, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.84892086330936, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.81620839363242, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 90.87136929460581, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.24012158054711, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 88.8728323699422, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.08763693270735, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.08306709265176, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.76, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.99497487437185, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.98648648648648, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.31740614334471, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.13242784380306, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 82.92682926829268, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.29642248722317, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.82608695652175, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.47643979057592, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.64726631393297, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 95.56313993174061, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 93.3786078098472, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 78.82352941176471, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.16788321167883, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.62049335863378, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.80239520958084, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 93.33333333333333, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.77327935222672, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.1769547325103, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.70689655172413, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.81181619256017, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.10514541387025, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.27360774818402, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.50617283950616, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.77450980392157, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.59367396593674, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 86.24454148471615, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 68.88111888111888, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 76.81451612903226, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.68073878627969, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.68073878627969, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 90.4645476772616, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.08994708994709, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.45504087193461, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 72.46963562753037, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 95.17426273458445, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.16201117318437, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.33333333333333, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.15730337078652, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.71751412429379, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.6878612716763, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 95.70200573065902, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.39024390243902, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.17629179331307, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.67637540453075, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.54088050314465, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.0392156862745, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.67549668874173, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 91.16719242902208, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.65034965034964, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 84.3076923076923, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 87.33766233766234, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.4375, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.25490196078431, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.55252918287937, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 74.45482866043614, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 62.20472440944882, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.6938775510204, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.84978540772532, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.22222222222223, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 84.70588235294117, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.53488372093024, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.15668202764977, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 95.04504504504504, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.52380952380952, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.96373056994818, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.44444444444444, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 80.25751072961373, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 72.8744939271255, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 77.19298245614034, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.41520467836257, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.02272727272727, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 88.13559322033898, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.875, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.35099337748345, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.22222222222221, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.32352941176471, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 99.21259842519686, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.38709677419355, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.36641221374046, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.36065573770492, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 86.25954198473282, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 93.69369369369369, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.42857142857143, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.96969696969697, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 98.38709677419355, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 90.9090909090909, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 96.42857142857143, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 86.79245283018868, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 81.48148148148148, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 97.5, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 45.67901234567901, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 94.87179487179486, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 45.714285714285715, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 54.54545454545454, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 85.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 86.66666666666667, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 65.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 36.36363636363637, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 44.44444444444444, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 92.3076923076923, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 100.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 34.78260869565217, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 42.857142857142854, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 33.33333333333333, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 0.0, \"Number of hospitals\": \"data completion\"}, {\"Percent of patients with data\": 0.0, \"Number of hospitals\": \"data completion\"}]}, \"selection\": {\"grid\": {\"bind\": \"scales\", \"type\": \"interval\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.json\", \"width\": 450, \"encoding\": {\"x\": {\"field\": \"Percent of patients with data\", \"bin\": {\"maxbins\": 10}, \"type\": \"quantitative\"}, \"y\": {\"type\": \"quantitative\", \"stack\": null, \"aggregate\": \"count\"}, \"color\": {\"field\": \"Number of hospitals\", \"type\": \"nominal\"}}};\n",
       "var selector = \"#14718fa3-9ada-4901-9cad-f3ce250d0a18\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#14718fa3-9ada-4901-9cad-f3ce250d0a18"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {
      "jupyter-vega3": "#14718fa3-9ada-4901-9cad-f3ce250d0a18"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['data completion']].vgplot.hist(bins=10,\n",
    "                                    var_name='Number of hospitals',\n",
    "                                    value_name='Percent of patients with data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most hospitals in eICU have good data coverage for the vitalaperiodic table."
   ]
  }
 ],
 "metadata": {
  "front-matter": {
   "date": "2015-09-01",
   "linktitle": "admissiondrug",
   "title": "admissiondrug"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
